In ResNet [1]}, He et al. proposed a residual learning framework, where a residual mapping is desired instead of fitting the whole underlying mapping (Fig. REF ). In SISR, as LR image and HR image share most of the same information, it is easy to explicitly model the residual image between LR and HR images. Residual learning enables deeper networks and remits the problem of gradient vanishing and degradation. With the help of residual learning, Kim [2]} proposed a very deep super-resolution network, also known as VDSR. For the convenience of network design, the residual block [1]} has gradually become the basic unit in the network structure. In the convolutional branch, it usually has two \(3 \times 3\)  convolutional layers, two batch normalization layers, and one ReLU activation function in between. It is worth noting that the batch normalization layer is often removed in the SISR task since EDSR [4]} points out that the batch normalization layer consumes more memory but will not improve the model performance.
